Utilizing machine learning to generate augmented reality delivery instructions for delivering an item to a location

ABSTRACT

A device receives delivery information indicating instructions for delivery of an item at a location, wherein the delivery information include an image of the location with a designated point for delivering the item. The device receives information indicating that a user device, associated with a delivery person, is at the location, and processes the delivery information and the information indicating that the user device, associated with the delivery person, is at the location, with a machine learning model, to generate delivery instructions for the item, wherein the delivery instructions include augmented reality information indicating the designated point for delivering the item at the location. The device provides the delivery instructions to the user device, wherein the delivery instructions enable the user device to utilize the augmented reality information to display the designated point for delivering the item within a live image of the location.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/122,476, filed Sep. 5, 2018, which is incorporated herein byreference.

BACKGROUND

More people than ever are shopping online and having items (e.g.,packages) delivered to their homes, workplaces, and/or other locations.Delivery services utilize delivery personnel to deliver the items to thehomes, the workplaces, and/or the other locations. If an item does notrequire a signature from a receiver of the item, the delivery personneltypically leave the item at a front door, in a mailbox, and/or the like.

SUMMARY

According to some implementations, a device may include one or morememories, and one or more processors, communicatively coupled to the oneor more memories, to receive delivery information indicatinginstructions for delivery of an item at a location, wherein the deliveryinformation may include an image of the location with a designated pointfor delivering the item. The one or more processors may receiveinformation indicating that a user device, associated with a deliveryperson, is at the location, and may process the delivery information andthe information indicating that the user device, associated with thedelivery person, is at the location, with a machine learning model, togenerate delivery instructions for the item, wherein the deliveryinstructions may include augmented reality information indicating thedesignated point for delivering the item at the location. The one ormore processors may provide the delivery instructions to the userdevice, wherein the delivery instructions may enable the user device toutilize the augmented reality information to display the designatedpoint for delivering the item within a live image of the location.

According to some implementations, a non-transitory computer-readablemedium may store instructions that include one or more instructionsthat, when executed by one or more processors of a device, cause the oneor more processors to receive registration information for registering auser associated with a location, and create an account for the userbased on the registration information. The one or more instructions maycause the one or more processors to receive, via the account, deliveryinformation indicating instructions for delivery of an item at thelocation, wherein the delivery information may include an image of thelocation with a designated point for delivering the item. The one ormore instructions may cause the one or more processors to determine thata user device, associated with a delivery person, is near the locationto deliver the item, wherein the user device, associated with thedelivery person, may be determined to be near the location based onglobal positioning system (GPS) coordinates of the user device. The oneor more instructions may cause the one or more processors to generatedelivery instructions for the item based on the delivery information andbased on determining that the user device, associated with the deliveryperson, is near the location, wherein the delivery instructions mayinclude augmented reality information indicating the designated pointfor delivering the item at the location. The one or more instructionsmay cause the one or more processors to provide the deliveryinstructions to the user device associated with the delivery person,wherein the delivery instructions may enable the user device, associatedwith the delivery person, to utilize the augmented reality informationto display the designated point for delivering the item within a liveimage of the location.

According to some implementations, a method may include receivingdelivery information indicating instructions for delivery of an item ata location, wherein the delivery information may include an image of thelocation with a designated point for delivering the item. The method mayinclude receiving information indicating that a user device, associatedwith a delivery person, will be delivering the item to the location at aparticular time, and generating delivery instructions for the item basedon the delivery information and the information indicating that the userdevice, associated with the delivery person, will be delivering the itemto the location at the particular time, wherein the deliveryinstructions may include augmented reality information indicating thedesignated point for delivering the item at the location. The method mayinclude providing the delivery instructions to the user device,associated with the delivery person, prior to the particular time,wherein the delivery instructions may enable the user device to utilizethe augmented reality information to display the designated point fordelivering the item within a live image of the location and at theparticular time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1H are diagrams of an example implementation described herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes for utilizing machinelearning to generate augmented reality delivery instructions fordelivering an item to a location.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

The increase in the quantity of packages being delivered has caused anincrease in a quantity of thefts of packages from homes, workplaces,and/or other locations. One way to reduce theft of an item is to requirea physical signature from a recipient of the item. However, this may beimpractical or impossible when the recipient is not at a deliverylocation when the package is scheduled to be delivered (e.g., in the daytime). Thus, delivery services currently do not provide a practical,safe, and convenient way of ensuring delivery of items to customers.

Some implementations described herein provide an augmented reality (AR)delivery platform that utilizes machine learning to generate augmentedreality delivery instructions for delivering an item to a location. Forexample, the AR delivery platform may receive delivery informationindicating instructions for delivery of an item at a location, whereinthe delivery information may include an image of the location with adesignated point for delivering the item. The AR delivery platform mayreceive information indicating that a user device, associated with adelivery person, is at the location, and may process the deliveryinformation and the information indicating that the user device,associated with the delivery person, is at the location, with a machinelearning model, to generate delivery instructions for the item, whereinthe delivery instructions may include augmented reality informationindicating the designated point for delivering the item at the location.The AR delivery platform may provide the delivery instructions to theuser device, wherein the delivery instructions may enable the userdevice to utilize the augmented reality information to display thedesignated point for delivering the item within a live image of thelocation.

In this way, the AR delivery platform provides a practical, safe, andconvenient way of ensuring delivery of items to customers. The ARdelivery platform may provide augmented reality information that ensuresdelivery of an item to a location designated by a customer, which mayprevent theft, vandalism, and/or destruction of the item and may reduceambiguity (e.g., for a delivery person) of where to deliver an item. Byensuring delivery of items to customers, the AR delivery platform mayconserve resources (e.g., processing resources, memory resources,transportation resources, manufacturing resources, and/or like) thatwould otherwise be used to replace stolen, vandalized, and/or destroyeditems.

FIGS. 1A-1H are diagrams of an example implementation 100 describedherein. As shown in FIGS. 1A-H, a user device, associated with a user(e.g., a customer, such as a home owner, a property manager, a deliveryservice, etc.), and another user device, associated with a deliveryperson, may be associated with an augmented reality (AR) deliveryplatform. As shown in FIG. 1A, the user of the user device may beassociated with a location (e.g., a home of the user). As further shownin FIG. 1A, and by reference number 105, the user of the user device mayutilize the user device to capture an image of the location to where adelivery is to be provided (e.g., at a later time).

In some implementations, the user may utilize the user device to provideinstructions for delivery of items at the location, such as instructionsto provide an item in a mailbox at the location, at a front door of thelocation, behind a screen door, on a porch of the location, behind aplant (e.g., a bush) at the location, within a lobby of the location, ata back door of the location, instructions to ring a doorbell and leavethe item at a front door if no one answers the doorbell, and/or thelike. In some implementations, as further shown in FIG. 1A, the user mayutilize the user device to provide indications (e.g., circles, points,squares, other shapes, colors, and/or the like), in the image of thelocation, that identify one or more designated points (e.g., squaresmarked “OK”) for delivering an item at the location, one or moredesignated points (e.g., squares marked “Not OK”) not to leave an itemat the location, and/or the like. In some implementations, suchindication may be provided based on the user walking to designatedpoints at the location and indicating whether a point is approved or notapproved for delivering an item. In some implementations, the userdevice may utilize the instructions for delivery of items at thelocation to automatically create the indications in the image of thelocation. For example, if the user provides instructions indicating thatan item is to be delivered to a side porch at the location, the userdevice may provide an indication at an image of the side porch in theimage of the location.

In some implementations, the user may utilize the user device to receivean application from the AR delivery platform, and may install theapplication on the user device. The application may enable the userdevice to capture the image of the location, receive the instructionsfor delivery of items at the location from the user, receive theindications, in the image of the location, that identify one or moredesignated points for delivering an item at the location, automaticallycreate the indications in the image of the location, and/or the like.

As shown in FIG. 1B, and by reference number 110, the AR deliveryplatform may receive, from the user device, registration information forregistering the user with the AR delivery platform. In someimplementations, the registration information may include informationindicating proof of an identity of the user (e.g., a name of the user, ahome address of the user, an email address of the user, a driver'slicense number of the user, and/or the like); information indicating thelocation of the user (e.g., a home address of the user, globalpositioning system (GPS) coordinates of the user device, an image ofmail indicating the location, and/or the like); information indicating amethod of payment for utilizing the AR delivery platform if payment isrequired (e.g., a credit card number, a debit card number, and/or thelike); and/or the like.

In some implementations, the AR delivery platform may validate that theregistration information is correct (e.g., that the user is properlyidentified, that the location of the user is verified and associatedwith the user, that the method of payment is valid, and/or the like),and may register the user with the AR delivery platform when theregistration information is correct. If the registration information isnot correct, the AR delivery platform may reject the request forregistering the user with the AR delivery platform. In someimplementations, when the user is registered with the AR deliveryplatform, the AR delivery platform may provide the application to theuser device (e.g., for capturing the image of the location, receivingthe instructions for delivery of items at the location from the user,receiving the indications, automatically creating the indications,and/or the like), as described above in connection with FIG. 1A.

As further shown in FIG. 1B, and by reference number 115, the ARdelivery platform may receive, from the user device, deliveryinformation indicating delivery instructions for the user (e.g., fordelivering items to the location). In some implementations, the user mayutilize the application to cause the user device to provide the deliveryinformation to the AR delivery platform. In some implementations, thedelivery information may include the image of the location; informationindicating preferred delivery times to the location; informationindicating specific delivery instructions (e.g., ring a doorbell, knockon a back door, and/or the like); the instructions for delivery of itemsat the location; the indications, in the image of the location, thatidentify one or more designated points for delivering an item at thelocation; the automatically created indications in the image of thelocation; and/or the like. In some implementations, the deliveryinstructions may be different for different sized items. For example, aletter may be placed in a mailbox or inside a screen door, while amedium or large package will need to be left at a different location(e.g., on a front porch).

As further shown in FIG. 1B, and by reference number 120, the ARdelivery platform may store the registration information and thedelivery information in a data structure (e.g., a database, a table, alist, and/or the like) associated with the AR delivery platform. In someimplementations, the AR delivery platform may utilize the registrationinformation and the delivery information stored in the data structure tocompare with a request for a delivery of an item, identify the user asbeing associated with the request, determine delivery instructions forthe item, and/or the like, as described below. For example, assume thatthe user orders goods (e.g., provided in a package) from an onlinecompany, and the online company provides the package to a deliveryservice that utilizes the AR delivery platform.

As shown in FIG. 1C, and by reference number 125, the AR deliveryplatform may receive, from the user device (e.g., an AR mobile device,AR glasses, and/or the like) associated with the delivery person (e.g.,of the delivery service), information indicating that the user deviceassociated with the delivery person is at the location. In someimplementations, the user device associated with the delivery person mayinclude an application (e.g., received from the AR delivery platform)that causes the user device to provide, to the AR delivery platform, theinformation indicating that the user device is at the location and toperform functions described below. In some implementations, the userdevice may automatically provide, to the AR delivery platform, theinformation indicating that the user device is at the location when GPScoordinates of the user device match GPS coordinates associated with thelocation. In some implementations, the user device may automaticallyprovide, to the AR delivery platform, GPS coordinates of the userdevice, and the AR delivery platform may determine that the user deviceis at the location when the GPS coordinates of the user device match theGPS coordinates associated with the location.

In some implementations, the user device may automatically provide, tothe AR delivery platform, images of an area around the user device. Insuch implementations, the AR delivery platform may determine that theuser device is at the location when the images of the area include animage of the location. For example, the AR delivery platform may analyzethe images of the area to identify an address number of the location(e.g., multiple address numbers may be associated with a single buildingin multi-tenant buildings), may determine that the images include theaddress (e.g., house number 125) when images show a house between housenumber 123 and house number 127, may analyze images of mailboxes or acurb to identify the address number of the location, and/or the like.

In some implementations, the delivery person may utilize the user deviceto capture an image of a shipping label associated with an item (e.g., acoded label, a label with an address, and/or the like), and maydetermine a location for the item based on the image (e.g., via opticalcharacter recognition and natural language processing, via bar codereader application, and/or the like). For example, the user device maydetermine which item is being delivered at any given time in order tonarrow an item to a specific location (e.g., when delivering items tomultiple townhouses together).

As further shown in FIG. 1C, the AR delivery platform may determinedelivery instructions for the package based on the delivery information(e.g., stored in the data structure) and based on receiving theinformation indicating that the user device associated with the deliveryperson is at the location. In some implementations, the AR deliveryplatform may map the information indicating that the user deviceassociated with the delivery person is at the location with the deliveryinformation associated with the location, and may generate the deliveryinstructions based on the mapped delivery information (e.g., thedelivery information provided by the user in FIG. 1B). In someimplementations, the delivery instructions may include augmented realityinformation, the image of the location, information indicating preferreddelivery times to the location, information indicating specific deliveryinstructions, the indications that identify one or more designatedpoints for delivering an item at the location, the indications thatidentify one or more designated points not to leave an item at thelocation, the automatically created indications in the image of thelocation, and/or the like. In some implementations, the delivery personmay indicate, to the AR delivery platform, what is being delivered(e.g., a size of an item, such as measurements of a package), and the ARdelivery platform may determine the delivery instructions based on thisadditional information. In some implementations, the augmented realityinformation may include the image of the location, the indications thatidentify one or more designated points for delivering an item at thelocation, the indications that identify one or more designated pointsnot to leave an item at the location, and/or the like.

In some implementations, the AR delivery platform may process thedelivery information associated with the location and the informationindicating that the user device, associated with the delivery person, isat the location, with a machine learning model, to generate the deliveryinstructions for the package. In some implementations, the machinelearning model may include a pattern recognition model that generatesthe delivery instructions for the package. For example, if the deliveryinformation does not indicate a designated point for delivery, themachine learning model may automatically select or recommend (e.g., tothe user) a designated point for delivery at the location. In anotherexample, if the delivery information indicates a designated point fordelivery, the machine learning model may determine whether thedesignated point for delivery is a best point for delivery (e.g., due tothefts near the location at such designated points), and may recommendone or more designated points for delivery to the user.

In some implementations, the AR delivery platform may perform a trainingoperation on the machine learning model with historical deliveryinformation associated with a geographical area that includes thelocation. The historical delivery information may include informationindicating delivery instructions for locations within the geographicalarea (e.g., if the location is a house, the geographical area mayinclude a neighborhood that includes the house), information indicatingdesignated points of delivery at the locations within the geographicalarea (e.g., most houses in the neighborhood have packages left at thefront door), information indicating theft, vandalism, and/or destructionof packages at the designated points of delivery at the locations withinthe geographical area, and/or the like. In some implementations, thehistorical delivery information may include information associated withother geographical areas.

The AR delivery platform may separate the historical deliveryinformation associated with the geographical area into a training set, avalidation set, a test set, and/or the like. In some implementations,the AR delivery platform may train the machine learning model using, forexample, an unsupervised training procedure and based on the historicaldelivery information associated with the geographical area. For example,the AR delivery platform may perform dimensionality reduction to reducethe historical delivery information associated with the geographicalarea to a minimum feature set, thereby reducing resources (e.g.,processing resources, memory resources, and/or the like) to train themachine learning model, and may apply a classification technique, to theminimum feature set.

In some implementations, the AR delivery platform may use a logisticregression classification technique to determine a categorical outcome(e.g., that the historical delivery information associated with thegeographical area indicate that certain designated points are ideal foritem delivery). Additionally, or alternatively, the AR delivery platformmay use a naïve Bayesian classifier technique. In this case, the ARdelivery platform may perform binary recursive partitioning to split thehistorical delivery information associated with the geographical areainto partitions and/or branches, and use the partitions and/or branchesto perform predictions (e.g., that the historical delivery informationassociated with the geographical area indicate that certain designatedpoints are ideal for item delivery). Based on using recursivepartitioning, the AR delivery platform may reduce utilization ofcomputing resources relative to manual, linear sorting and analysis ofdata points, thereby enabling use of thousands, millions, or billions ofdata points to train the machine learning model, which may result in amore accurate model than using fewer data points.

Additionally, or alternatively, the AR delivery platform may use asupport vector machine (SVM) classifier technique to generate anon-linear boundary between data points in the training set. In thiscase, the non-linear boundary is used to classify test data into aparticular class.

Additionally, or alternatively, the AR delivery platform may train themachine learning model using a supervised training procedure thatincludes receiving input to the machine learning model from a subjectmatter expert, which may reduce an amount of time, an amount ofprocessing resources, and/or the like to train the machine learningmodel of activity automatability relative to an unsupervised trainingprocedure. In some implementations, the AR delivery platform may use oneor more other model training techniques, such as a neural networktechnique, a latent semantic indexing technique, and/or the like. Forexample, the AR delivery platform may perform an artificial neuralnetwork processing technique (e.g., using a two-layer feedforward neuralnetwork architecture, a three-layer feedforward neural networkarchitecture, and/or the like) to perform pattern recognition withregard to patterns of the historical delivery information associatedwith the geographical area. In this case, using the artificial neuralnetwork processing technique may improve an accuracy of the trainedmachine learning model generated by the AR delivery platform by beingmore robust to noisy, imprecise, or incomplete data, and by enabling theAR delivery platform to detect patterns and/or trends undetectable tohuman analysts or systems using less complex techniques.

In some implementations, when no delivery information is provided for alocation, the delivery person may utilize the user device to capture animage of the location and to identify a designated point of the packagein the image. The delivery person may provide the image of the locationand the designated point of the package in the image to the AR deliveryplatform. The AR delivery platform may process the image of the locationand the designated point of the package in the image, with the machinelearning model, to generate augmented reality information ornon-augmented reality information that may be utilized by a receiver ofthe package (e.g., the homeowner) to retrieve the package at thelocation.

As further shown in FIG. 1C, and by reference number 130, the ARdelivery platform may provide the delivery instructions, with theaugmented reality information, to the user device associated with thedelivery person. The user device may receive the delivery instructionsand the augmented reality information, and the delivery instructions andthe augmented reality information may enable the delivery person todeliver the package to an appropriate point at the location. In someimplementations, prior to traveling to the location, the AR deliveryplatform may provide the delivery instructions, with the augmentedreality information, to the user device if the user device indicatesspecific delivery locations to be performed by the delivery person. Forexample, if the delivery person is making ten deliveries to locations,the delivery person may cause the user device to provide informationassociated with the ten deliveries (e.g., locations, order of thedeliveries, and/or the like) to the AR delivery platform. As thedelivery person travels to each of the ten locations, the AR deliveryplatform may automatically preload (e.g., on the user device) thedelivery instructions, with the augmented reality information, for eachlocation as the user device approaches each location.

In some implementations, the user device may map the informationindicating that the user device is at the location with the deliveryinformation associated with the location (e.g., provided in the datastructure). In such implementations, the user device may generate thedelivery instructions, with the augmented reality information, based onthe mapped delivery information (e.g., the delivery information providedby the user in FIG. 1B).

As shown in FIG. 1D, and by reference number 135, the user deviceassociated with the delivery person may utilize the augmented realityinformation to display the delivery instructions for the package andensure proper delivery of the package. For example, if the user deviceis smart glasses, the delivery person may wear the smart glasses andlook at the location. The smart glasses may display the live location(e.g., the user's house), the delivery instructions (e.g., “Ringdoorbell. If not home leave at designated location”), and the augmentedreality information with the live location. The augmented realityinformation may include designated points or areas (e.g., red Not OKareas) to not deliver the package, and designated points or areas (e.g.,green OK areas) to deliver the package in the live location. Thedelivery person may walk to the designated area (e.g., the green OKarea) and may deliver the package at the designated area. In anotherexample, if the user device is a mobile device, the delivery person maycapture the live location with the mobile device, and the mobile devicemay display the live location, the delivery instructions, and theaugmented reality information with the live location. The augmentedreality information may include the designated points or areas (e.g.,red Not OK areas) to not deliver the package, and the designated pointsor areas (e.g., green OK areas) to deliver the package in the livelocation. The delivery person may walk with the mobile device to thedesignated area (e.g., the green OK area) and may delivery the packageat the designated area.

As shown in FIG. 1E, assume that the location includes handwritteninstructions that the delivery person cannot understand, that thedelivery person does not want to reference handwritten instructions eachtime at the location and does not remember the handwritten instructions,that the handwritten instructions are not noticeable by the deliveryperson by are captured by the user device, and/or the like. In such asituation, and as shown by reference number 140 in FIG. 1E, the ARdelivery platform may receive, from the user device associated with thedelivery person, an image of the handwritten instructions andinformation indicating a location of the user device. In someimplementations, the AR delivery platform may perform optical characterrecognition (OCR) on the image to generate text of the handwritteninstructions. In some implementations, the OCR may convert the imageinto an electronic format (e.g., the text). Optical characterrecognition involves a conversion of images of typed, handwritten, orprinted text into machine-encoded text. For example, OCR may be appliedto a scanned document, a photo of a document, a photo of a scene thatincludes text, and/or the like, to produce electronic data (e.g., textdata). Implementations of OCR may employ pattern recognition, artificialintelligence, computer vision, and/or the like.

In some implementations, the AR delivery platform may perform naturallanguage processing (NLP) on the text, provided by the OCR, to generateresults (e.g., a translation of the handwritten instructions, “Knock onthe front door. If no one is home, leave the package next to thegarage.”). For example, the AR delivery platform may apply naturallanguage processing to interpret the text and generate additionalinformation associated with the potential meaning of information withinthe text. Natural language processing involves techniques performed(e.g., by a computer system) to analyze, understand, and derive meaningfrom human language in a useful way. Natural language processing can beapplied to analyze text, allowing machines to understand how humansspeak, enabling real world applications such as automatic textsummarization, sentiment analysis, topic extraction, named entityrecognition, parts-of-speech tagging, relationship extraction, stemming,and/or the like.

In some implementations, the AR delivery platform may determine deliveryinstructions for the package based on the results of the OCR and thenatural language processing and based on the location. For example, theAR delivery platform may map the information indicating the location ofthe user device with the delivery information associated with thelocation, and may generate the delivery instructions based on the mappeddelivery information (e.g., the delivery information provided by theuser in FIG. 1B). In some implementations, the delivery instructions mayinclude augmented reality information, the image of the location,information indicating preferred delivery times to the location,information indicating specific delivery instructions, the indicationsthat identify one or more designated points for delivering an item atthe location, and/or the like. In some implementations, the deliveryinstructions may be altered based on the information determined from thehandwritten instructions. For example, if the delivery instructionsindicate that the package should be delivered to the front door and thehandwritten instructions indicate that the package should be deliverednext to a garage (e.g., “Knock on the front door. If no one is home,leave the package next to the garage.”), the delivery instructions maybe altered to indicate that the package should be delivered next to thegarage. In such implementations, the AR delivery platform may alter theaugmented reality information associated with the delivery to indicatedelivery next to the garage.

In some implementations, the user device associated with the deliveryperson may perform the OCR and natural language processing on thehandwritten instructions, and may alter the delivery instructions basedon the results of the OCR and natural language processing. In someimplementations, if the OCR and natural language processing fails totranslate the handwritten instructions, the handwritten instructions maybe manually translated.

As further shown in FIG. 1E, and by reference number 145, the ARdelivery platform may provide the delivery instructions, with theaugmented reality information, to the user device associated with thedelivery person. The user device may receive the delivery instructionsand the augmented reality information, and the delivery instructions andthe augmented reality information may enable the delivery person todeliver the package to an appropriate point at the location.

As shown in FIG. 1F, and by reference number 150, the user deviceassociated with the delivery person may utilize the augmented realityinformation to display the delivery instructions for the package andensure proper delivery of the package. For example, if the user deviceis smart glasses, the delivery person may wear the smart glasses andlook at the location. The smart glasses may display the live location(e.g., the user's house), the delivery instructions (e.g., “Knock on thefront door. If no one is home, leave the package next to the garage.”),and the augmented reality information with the live location. Theaugmented reality information may include designated points or areas(e.g., red Not OK areas) to not deliver the package, and designatedpoints or areas (e.g., green OK areas) to deliver the package in thelive location. The delivery person may walk to the designated area(e.g., the green OK area) and may deliver the package at the designatedarea (e.g., next to the garage). In another example, if the user deviceis a mobile device, the delivery person may capture the live locationwith the mobile device, and the mobile device may display the livelocation, the delivery instructions, and the augmented realityinformation with the live location. The augmented reality informationmay include the designated points or areas (e.g., red Not OK areas) tonot deliver the package, and the designated points or areas (e.g., greenOK areas) to deliver the package in the live location. The deliveryperson may walk with the mobile device to the designated area (e.g., thegreen OK area) and may deliver the package at the designated area (e.g.,next to the garage).

As shown in FIG. 1G, and by reference number 155, the AR deliveryplatform may receive images of a location over a period of time. In someimplementations, the received images may indicate that the location haschanged over the period of time. As further shown in FIG. 1G, and byreference number 160, the AR delivery platform may generate and train amachine learning model based on the images of the location over theperiod of time. In some implementations, the AR delivery platform mayutilize the machine learning model (e.g., a pattern recognition model)to identify the patterns in the image of the location, to process thedelivery information and the information indicating that the userdevice, associated with the delivery person, is at the location, togenerate delivery instructions for the item, and/or the like.

In some implementations, the AR delivery platform may perform a trainingoperation on the machine learning model with the images of the locationover the period of time. For example, the AR delivery platform mayseparate the images of the location over the period of time into atraining set, a validation set, a test set, and/or the like. In someimplementations, the AR delivery platform may train the machine learningmodel using, for example, an unsupervised training procedure and basedon the training set of the images of the location over the period oftime. For example, the AR delivery platform may perform dimensionalityreduction to reduce the images of the location over the period of timeto a minimum feature set, thereby reducing resources (e.g., processingresources, memory resources, and/or the like) to train the machinelearning model, and may apply a classification technique, to the minimumfeature set.

In some implementations, the AR delivery platform may use a logisticregression classification technique to determine a categorical outcome(e.g., that the images of the location over the period of time indicatechanges to the location). Additionally, or alternatively, the ARdelivery platform may use a naïve Bayesian classifier technique. In thiscase, the AR delivery platform may perform binary recursive partitioningto split the images of the location over the period of time intopartitions and/or branches, and use the partitions and/or branches toperform predictions (e.g., that the images of the location over theperiod of time indicate changes to the location). Based on usingrecursive partitioning, the AR delivery platform may reduce utilizationof computing resources relative to manual, linear sorting and analysisof data points, thereby enabling use of thousands, millions, or billionsof data points to train the machine learning model, which may result ina more accurate model than using fewer data points.

Additionally, or alternatively, the AR delivery platform may use asupport vector machine (SVM) classifier technique to generate anon-linear boundary between data points in the training set. In thiscase, the non-linear boundary is used to classify test data into aparticular class.

Additionally, or alternatively, the AR delivery platform may train themachine learning model using a supervised training procedure thatincludes receiving input to the machine learning model from a subjectmatter expert, which may reduce an amount of time, an amount ofprocessing resources, and/or the like to train the machine learningmodel of activity automatability relative to an unsupervised trainingprocedure. In some implementations, the AR delivery platform may use oneor more other model training techniques, such as a neural networktechnique, a latent semantic indexing technique, and/or the like. Forexample, the AR delivery platform may perform an artificial neuralnetwork processing technique (e.g., using a two-layer feedforward neuralnetwork architecture, a three-layer feedforward neural networkarchitecture, and/or the like) to perform pattern recognition withregard to patterns of the images of the location over the period oftime. In this case, using the artificial neural network processingtechnique may improve an accuracy of the trained machine learning modelgenerated by the AR delivery platform by being more robust to noisy,imprecise, or incomplete data, and by enabling the AR delivery platformto detect patterns and/or trends undetectable to human analysts orsystems using less complex techniques.

As further shown in FIG. 1G, and by reference number 165, the ARdelivery platform may utilize the trained machine learning model todetermine that a new garage has been added at the location. The ARdelivery platform may also determine that packages are now delivered atthe newly added garage. In some implementations, the AR deliveryplatform may alter delivery instructions for the location to indicatethat packages are to be delivered at the newly added garage (e.g.,rather than the front door).

With reference to FIG. 1H, assume that the delivery person is to deliveran item to a location within a secure building (e.g., an officebuilding). As shown in FIG. 1H, and by reference number 170, the ARdelivery platform may provide, to the user device associated with thedelivery person, delivery instructions that include walking directionsto the location within the building and dynamic codes for accessing thebuilding and/or parts of the building. For example, the deliveryinstructions may include augmented reality information indicating thewalking directions (e.g., proceed straight, a straight arrow, and/or thelike), the dynamic codes for accessing the building and/or parts of thebuilding (e.g., use code 0654 to access the door), and/or the like. Insome implementations, the codes may be dynamic in that they may only beused once and during the time the delivery person is within the building(e.g., to maintain security in the building). In this way, the ARdelivery platform may provide augmented reality information that helps adelivery person to navigate a complex and secure building quickly andeasily.

In this way, several different stages of the process for utilizingmachine learning to generate augmented reality delivery instructions fordelivering an item to a location are automated, which may remove humansubjectivity and waste from the process, and which may improve speed andefficiency of the process and conserve computing resources (e.g.,processing resources, memory resources, and/or the like). Furthermore,implementations described herein use a rigorous, computerized process toperform tasks or roles that were not previously performed or werepreviously performed using subjective human intuition or input. Forexample, currently there does not exist a technique that utilizesmachine learning to generate augmented reality delivery instructions fordelivering an item to a location. Finally, automating the process forutilizing machine learning to generate augmented reality deliveryinstructions for delivering an item to a location conserves computingresources (e.g., processing resources, memory resources, and/or thelike) that would otherwise be wasted in attempting to provide deliveryinstructions for delivering an item to a location.

As indicated above, FIGS. 1A-1H are provided merely as examples. Otherexamples are possible and may differ from what was described with regardto FIGS. 1A-1H.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a user device 210, an AR deliveryplatform 220, and a network 230. Devices of environment 200 mayinterconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

User device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, user device 210 may include amobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptopcomputer, a tablet computer, a desktop computer, a handheld computer, agaming device, a wearable communication device (e.g., a smartwristwatch, a pair of smart eyeglasses, etc.), or a similar type ofdevice. In some implementations, user device 210 may receive informationfrom and/or transmit information to AR delivery platform 220.

AR delivery platform 220 includes one or more devices that utilizemachine learning to generate augmented reality delivery instructions fordelivering an item to a location. In some implementations, AR deliveryplatform 220 may be designed to be modular such that certain softwarecomponents may be swapped in or out depending on a particular need. Assuch, AR delivery platform 220 may be easily and/or quickly reconfiguredfor different uses. In some implementations, AR delivery platform 220may receive information from and/or transmit information to one or moreuser devices 210.

In some implementations, as shown, AR delivery platform 220 may behosted in a cloud computing environment 222. Notably, whileimplementations described herein describe AR delivery platform 220 asbeing hosted in cloud computing environment 222, in someimplementations, AR delivery platform 220 may not be cloud-based (i.e.,may be implemented outside of a cloud computing environment) or may bepartially cloud-based.

Cloud computing environment 222 includes an environment that hosts ARdelivery platform 220. Cloud computing environment 222 may providecomputation, software, data access, storage, etc. services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that host AR delivery platform 220. As shown,cloud computing environment 222 may include a group of computingresources 224 (referred to collectively as “computing resources 224” andindividually as “computing resource 224”).

Computing resource 224 includes one or more personal computers,workstation computers, server devices, or other types of computationand/or communication devices. In some implementations, computingresource 224 may host AR delivery platform 220. The cloud resources mayinclude compute instances executing in computing resource 224, storagedevices provided in computing resource 224, data transfer devicesprovided by computing resource 224, etc. In some implementations,computing resource 224 may communicate with other computing resources224 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2, computing resource 224 includes a group ofcloud resources, such as one or more applications (“APPs”) 224-1, one ormore virtual machines (“VMs”) 224-2, virtualized storage (“VSs”) 224-3,one or more hypervisors (“HYPs”) 224-4, and/or the like.

Application 224-1 includes one or more software applications that may beprovided to or accessed by user device 210. Application 224-1 mayeliminate a need to install and execute the software applications onuser device 210. For example, application 224-1 may include softwareassociated with AR delivery platform 220 and/or any other softwarecapable of being provided via cloud computing environment 222. In someimplementations, one application 224-1 may send/receive informationto/from one or more other applications 224-1, via virtual machine 224-2.

Virtual machine 224-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 224-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 224-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 224-2 may execute on behalf of a user(e.g., a user of user device 210 or an operator of AR delivery platform220), and may manage infrastructure of cloud computing environment 222,such as data management, synchronization, or long-duration datatransfers.

Virtualized storage 224-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 224. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 224-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 224.Hypervisor 224-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210, AR delivery platform 220, and/orcomputing resource 224. In some implementations, user device 210, ARdelivery platform 220, and/or computing resource 224 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3, device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface,and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for utilizing machinelearning to generate augmented reality delivery instructions fordelivering an item to a location. In some implementations, one or moreprocess blocks of FIG. 4 may be performed by an AR delivery platform(e.g., AR delivery platform 220). In some implementations, one or moreprocess blocks of FIG. 4 may be performed by another device or a groupof devices separate from or including AR delivery platform 220, such asuser device 210.

As shown in FIG. 4, process 400 may include receiving deliveryinformation indicating instructions for delivery of an item at alocation, wherein the delivery information includes an image of thelocation with a designated point for delivering the item (block 410).For example, the AR delivery platform (e.g., using computing resource224, processor 320, communication interface 370, and/or the like) mayreceive delivery information indicating instructions for delivery of anitem at a location, as described above in connection with FIGS. 1A-2. Insome implementations, the delivery information may include an image ofthe location with a designated point for delivering the item.

As further shown in FIG. 4, process 400 may include receivinginformation indicating that a user device, associated with a deliveryperson, is at the location (block 420). For example, the AR deliveryplatform (e.g., using computing resource 224, processor 320, memory 330,communication interface 370, and/or the like) may receive informationindicating that a user device, associated with a delivery person, is atthe location, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include processing thedelivery information and the information indicating that the userdevice, associated with the delivery person, is at the location, with amachine learning model, to generate delivery instructions for the item,wherein the delivery instructions include augmented reality informationindicating the designated point for delivering the item at the location(block 430). For example, the AR delivery platform (e.g., usingcomputing resource 224, processor 320, storage component 340, and/or thelike) may process the delivery information and the informationindicating that the user device, associated with the delivery person, isat the location, with a machine learning model, to generate deliveryinstructions for the item, as described above in connection with FIGS.1A-2. In some implementations, the delivery instructions may includeaugmented reality information indicating the designated point fordelivering the item at the location.

As further shown in FIG. 4, process 400 may include providing thedelivery instructions to the user device, wherein the deliveryinstructions enable the user device to utilize the augmented realityinformation to display the designated point for delivering the itemwithin a live image of the location (block 440). For example, the ARdelivery platform (e.g., using computing resource 224, processor 320,memory 330, communication interface 370, and/or the like) may providethe delivery instructions to the user device, as described above inconnection with FIGS. 1A-2. In some implementations, the deliveryinstructions may enable the user device to utilize the augmented realityinformation to display the designated point for delivering the itemwithin a live image of the location.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the AR delivery platform may receive an imageof handwritten instructions provided at the location, may perform imagerecognition on the image of the handwritten instructions to generatetext, may perform natural language processing of the text to generateresults, may generate additional delivery instructions for the itembased on the results, and may provide the additional deliveryinstructions to the user device.

In some implementations, the AR delivery platform may receive images ofthe location over a period of time, may generate and train anothermachine learning model based on the images of the location over theperiod of time to produce a trained machine learning model, may generateadditional delivery instructions for the item based on the trainedmachine learning model, and may provide the additional deliveryinstructions to the user device.

In some implementations, the location may include a building, and the ARdelivery platform may generate additional delivery instructions thatinclude walking directions to the designated point and one or more codesfor accessing one or more portions of the building, and may provide theadditional delivery instructions to the user device. In someimplementations, when processing the delivery information, the ARdelivery platform may match information identifying the location and thedelivery information indicating instructions for delivery of the item atthe location, and may generate the delivery instructions for the itembased on matching the information identifying the location and thedelivery information.

In some implementations, the AR delivery platform may receive additionaldelivery information indicating instructions for delivery of a pluralityof items at a plurality of locations, may store the additional deliveryinformation and the delivery information in a data structure, maycompare information identifying the location with the additionaldelivery information and the delivery information stored in the datastructure, and may identify the delivery information in the datastructure based on comparing the information identifying the locationwith the additional delivery information and the delivery informationstored in the data structure. In some implementations, when generatingthe delivery instructions for the item, the AR delivery platform maygenerate the delivery instructions for the item based on identifying thedelivery information in the data structure.

In some implementations, the AR delivery platform may receiveregistration information for registering the location, and may create anaccount for the location based on the registration information. In someimplementations, when receiving the delivery information indicating theinstructions for delivery of the item at the location, the AR deliveryplatform may receive the delivery information via the account for thelocation.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for utilizing machinelearning to generate augmented reality delivery instructions fordelivering an item to a location. In some implementations, one or moreprocess blocks of FIG. 5 may be performed by an AR delivery platform(e.g., AR delivery platform 220). In some implementations, one or moreprocess blocks of FIG. 5 may be performed by another device or a groupof devices separate from or including AR delivery platform 220, such asuser device 210.

As shown in FIG. 5, process 500 may include receiving registrationinformation for registering a user associated with a location (block510). For example, the AR delivery platform (e.g., using computingresource 224, processor 320, communication interface 370, and/or thelike) may receive registration information for registering a userassociated with a location, as described above in connection with FIGS.1A-2.

As further shown in FIG. 5, process 500 may include creating an accountfor the user based on the registration information (block 520). Forexample, the AR delivery platform (e.g., using computing resource 224,processor 320, storage component 340, and/or the like) may create anaccount for the user based on the registration information, as describedabove in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include receiving, via theaccount, delivery information indicating instructions for delivery of anitem at the location, wherein the delivery information includes an imageof the location with a designated point for delivering the item (block530). For example, the AR delivery platform (e.g., using computingresource 224, processor 320, memory 330, communication interface 370,and/or the like) may receive, via the account, delivery informationindicating instructions for delivery of an item at the location, asdescribed above in connection with FIGS. 1A-2. In some implementations,the delivery information may include an image of the location with adesignated point for delivering the item.

As further shown in FIG. 5, process 500 may include determining that auser device, associated with a delivery person, is near the location todeliver the item, wherein the user device, associated with the deliveryperson, is determined to be near the location based on globalpositioning system (GPS) coordinates of the user device (block 540). Forexample, the AR delivery platform (e.g., using computing resource 224,processor 320, storage component 340, and/or the like) may determinethat a user device, associated with a delivery person, is near thelocation to deliver the item, as described above in connection withFIGS. 1A-2. In some implementations, the user device, associated withthe delivery person, may be determined to be near the location based onglobal positioning system (GPS) coordinates of the user device.

As further shown in FIG. 5, process 500 may include generating deliveryinstructions for the item based on the delivery information and based ondetermining that the user device, associated with the delivery person,is near the location, wherein the delivery instructions includeaugmented reality information indicating the designated point fordelivering the item at the location (block 550). For example, the ARdelivery platform (e.g., using computing resource 224, processor 320,memory 330, and/or the like) may generate delivery instructions for theitem based on the delivery information and based on determining that theuser device, associated with the delivery person, is near the location,as described above in connection with FIGS. 1A-2. In someimplementations, the delivery instructions may include augmented realityinformation indicating the designated point for delivering the item atthe location.

As further shown in FIG. 5, process 500 may include providing thedelivery instructions to the user device associated with the deliveryperson, wherein the delivery instructions enable the user device,associated with the delivery person, to utilize the augmented realityinformation to display the designated point for delivering the itemwithin a live image of the location (block 560). For example, the ARdelivery platform (e.g., using computing resource 224, processor 320,memory 330, communication interface 370, and/or the like) may providethe delivery instructions to the user device associated with thedelivery person, as described above in connection with FIGS. 1A-2. Insome implementations, the delivery instructions may enable the userdevice, associated with the delivery person, to utilize the augmentedreality information to display the designated point for delivering theitem within a live image of the location.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the AR delivery platform may receive additionaldelivery information indicating instructions for delivery of a pluralityof items at a plurality of locations, may store the additional deliveryinformation and the delivery information in a data structure, maycompare the GPS coordinates of the user device with the additionaldelivery information and the delivery information stored in the datastructure, and may identify the delivery information in the datastructure based on comparing the GPS coordinates of the user device withthe additional delivery information and the delivery information storedin the data structure. In some implementations, the AR deliveryplatform, when generating the delivery instructions for the item, maygenerate the delivery instructions for the item based on identifying thedelivery information in the data structure.

In some implementations, the user device, associated with the deliveryperson, may include a tablet computer, a mobile device, or smartglasses. In some implementations, the AR delivery platform may receiveimages of the location over a period of time, and may generate and traina machine learning model based on the images of the location over theperiod of time to produce a trained machine learning model. In someimplementations, when generating the delivery instructions for the item,the AR delivery platform may generate the delivery instructions for theitem based on the trained machine learning model.

In some implementations, the AR delivery platform may receive an imageof handwritten instructions provided at the location, may perform imagerecognition on the image of the handwritten instructions to generatetext, and may perform natural language processing of the text togenerate results. In some implementations, when generating the deliveryinstructions for the item, the AR delivery platform may generate thedelivery instructions for the item based on the results of the naturallanguage processing of the handwritten instructions.

In some implementations, the location may include a building, and the ARdelivery platform may generate additional delivery instructions thatinclude walking directions to the designated point and one or more codesfor accessing one or more portions of the building, and may provide theadditional delivery instructions to the user device. In someimplementations, when generating the delivery instructions for the item,the AR delivery platform may match the GPS coordinates of the userdevice and the delivery information indicating instructions for deliveryof the item at the location, and may generate the delivery instructionsfor the item based on matching the GPS coordinates of the user deviceand the delivery information.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for utilizing machinelearning to generate augmented reality delivery instructions fordelivering an item to a location. In some implementations, one or moreprocess blocks of FIG. 6 may be performed by an AR delivery platform(e.g., AR delivery platform 220). In some implementations, one or moreprocess blocks of FIG. 6 may be performed by another device or a groupof devices separate from or including AR delivery platform 220, such asuser device 210.

As shown in FIG. 6, process 600 may include receiving deliveryinformation indicating instructions for delivery of an item at alocation, wherein the delivery information includes an image of thelocation with a designated point for delivering the item (block 610).For example, the AR delivery platform (e.g., using computing resource224, processor 320, communication interface 370, and/or the like) mayreceive delivery information indicating instructions for delivery of anitem at a location, as described above in connection with FIGS. 1A-2. Insome implementations, the delivery information may include an image ofthe location with a designated point for delivering the item.

As further shown in FIG. 6, process 600 may include receivinginformation indicating that a user device, associated with a deliveryperson, will be delivering the item to the location at a particular time(block 620). For example, the AR delivery platform (e.g., usingcomputing resource 224, processor 320, storage component 340,communication interface 370, and/or the like) may receive informationindicating that a user device, associated with a delivery person, willbe delivering the item to the location at a particular time, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include generating deliveryinstructions for the item based on the delivery information and theinformation indicating that the user device, associated with thedelivery person, will be delivering the item to the location at theparticular time, wherein the delivery instructions include augmentedreality information indicating the designated point for delivering theitem at the location (block 630). For example, the AR delivery platform(e.g., using computing resource 224, processor 320, memory 330, and/orthe like) may generate delivery instructions for the item based on thedelivery information and the information indicating that the userdevice, associated with the delivery person, will be delivering the itemto the location at the particular time, as described above in connectionwith FIGS. 1A-2. In some implementations, the delivery instructions mayinclude augmented reality information indicating the designated pointfor delivering the item at the location.

As further shown in FIG. 6, process 600 may include providing thedelivery instructions to the user device, associated with the deliveryperson, prior to the particular time, wherein the delivery instructionsenable the user device to utilize the augmented reality information todisplay the designated point for delivering the item within a live imageof the location and at the particular time (block 640). For example, theAR delivery platform (e.g., using computing resource 224, processor 320,memory 330, communication interface 370, and/or the like) may providethe delivery instructions to the user device, associated with thedelivery person, prior to the particular time, as described above inconnection with FIGS. 1A-2. In some implementations, the deliveryinstructions may enable the user device to utilize the augmented realityinformation to display the designated point for delivering the itemwithin a live image of the location and at the particular time.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the AR delivery platform may receive an imageof handwritten instructions provided at the location, may perform imagerecognition on the image of the handwritten instructions to generatetext, may perform natural language processing of the text to generateresults, may generate additional delivery instructions for the itembased on the results, and may provide the additional deliveryinstructions to the user device associated with the delivery person. Insome implementations, the delivery instructions may enable the userdevice to utilize the augmented reality information to display thedesignated point for delivering the item within the live image of thelocation when the user device is located at the location.

In some implementations, the AR delivery platform may receive images ofthe location over a period of time, and may generate and train a machinelearning model based on the images of the location over the period oftime to produce a trained machine learning model. In someimplementations, when generating the delivery instructions for the item,the AR delivery platform may generate the delivery instructions for theitem based on information generated by the trained machine learningmodel.

In some implementations, the AR delivery platform may receive additionaldelivery information indicating instructions for delivery of a pluralityof items at a plurality of locations, may store the additional deliveryinformation and the delivery information in a data structure, maycompare the information indicating that the user device, associated withthe delivery person, will be delivering the item to the location withthe additional delivery information and the delivery information storedin the data structure, and may identify the delivery information in thedata structure based on comparing the information indicating that theuser device, associated with the delivery person, will be delivering theitem to the location with the additional delivery information and thedelivery information stored in the data structure. In someimplementations, when generating the delivery instructions for the item,the AR delivery platform may generate the delivery instructions for theitem based on identifying the delivery information in the datastructure.

In some implementations, the AR delivery platform may receiveregistration information for registering a user associated with thelocation, and may create an account for the user based on theregistration information. In some implementations, when receiving thedelivery information indicating the instructions for delivery of theitem at the location, the AR delivery platform may receive the deliveryinformation via the account for the user.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, or the like.A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: one or more memories; andone or more processors, communicatively coupled to the one or morememories, to: receive delivery information indicating instructions fordelivery of an item at a location; receive images of the location thatwere captured at different times; train a machine learning model, basedon the images of the location that were captured at different times, toproduce a trained machine learning model; determine that a physicalattribute of the location has changed over a period of time based on thetrained machine learning model; automatically select, using a patternrecognition model of the trained machine learning model, a designatedpoint for delivering the item at the location based on the physicalattribute of the location that changed; generate delivery instructionsfor the item based on the delivery information and the designated pointfor delivering the item; and provide the delivery instructions to amobile device associated with a delivery service.
 2. The device of claim1, wherein the delivery instructions include augmented realityinformation indicating the designated point for delivering the item atthe location.
 3. The device of claim 1, wherein the one or moreprocessors are to: receive information indicating that the mobiledevice, associated with the delivery service, is at the location,wherein the delivery instructions are provided to the mobile device,associated with the delivery service, based on the informationindicating that the mobile device, associated with the delivery service,is at the location.
 4. The device of claim 1, wherein the one or moreprocessors, when training the machine learning model, are to: performdimensionality reduction to reduce the images of the location to afeature set of images; and train the machine learning model using thefeature set of images.
 5. The device of claim 4, wherein the one or moreprocessors are further to: apply a classification technique to thefeature set of images, the classification technique including: alogistic regression classification technique, a naïve Bayesianclassifier technique, binary recursive partitioning, or a support vectormachine (SVM) classifier technique.
 6. The device of claim 1, wherein atleast one of the images of the location are received from a mobiledevice associated with a person at the location.
 7. The device of claim1, wherein the one or more processors are further to: receiveregistration information for registering the location; and create anaccount for the location based on the registration information, wherein,when receiving the delivery information indicating the instructions fordelivery of the item at the location, the one or more processors are to:receive the delivery information via the account for the location.
 8. Amethod, comprising: receiving, by a device, delivery informationindicating instructions for delivery of an item at a location;receiving, by the device, images of the location that were captured atdifferent times; training, by the device, a machine learning model,based on the images of the location that were captured at differenttimes, to produce a trained machine learning model; determining, by thedevice, that a physical attribute of the location has changed over aperiod of time based on the trained machine learning model;automatically selecting, by the device and using a pattern recognitionmodel of the trained machine learning model, a designated point fordelivering the item at the location based on the physical attribute ofthe location that changed; generating, by the device, deliveryinstructions for the item based on the delivery information and thedesignated point for delivering the item; and providing, by the device,the delivery instructions to a mobile device associated with a deliveryservice.
 9. The method of claim 8, wherein the delivery instructionsinclude augmented reality information indicating the designated pointfor delivering the item at the location.
 10. The method of claim 8,further comprising receiving information indicating that the mobiledevice, associated with the delivery service, is at the location,wherein the delivery instructions are provided to the mobile device,associated with the delivery service, based on the informationindicating that the mobile device, associated with the delivery service,is at the location.
 11. The method of claim 8, wherein training themachine learning model includes: performing dimensionality reduction toreduce the images of the location to a feature set of images; andtraining the machine learning model using the feature set of images. 12.The method of claim 11, further comprising: applying a classificationtechnique to the feature set of images, the classification techniqueincluding: a logistic regression classification technique, a naïveBayesian classifier technique, binary recursive partitioning, or asupport vector machine (SVM) classifier technique.
 13. The method ofclaim 8, wherein at least one of the images of the location are receivedfrom a mobile device associated with a person at the location.
 14. Themethod of claim 8, further comprising: receiving registrationinformation for registering the location; and creating an account forthe location based on the registration information, wherein receivingthe delivery information indicating the instructions for delivery of theitem at the location includes: receiving the delivery information viathe account for the location.
 15. A non-transitory computer-readablemedium storing instructions, the instructions comprising: one or moreinstructions that, when executed by one or more processors, cause theone or more processors to: receive delivery information indicatinginstructions for delivery of an item at a location; receive images ofthe location that were captured at different times; train a machinelearning model, based on the images of the location that were capturedat different times, to produce a trained machine learning model;determine that a physical attribute of the location has changed over aperiod of time based on the trained machine learning model;automatically select, using a pattern recognition model of the trainedmachine learning model, a designated point for delivering the item atthe location based on the physical attribute of the location thatchanged; generate delivery instructions for the item based on thedelivery information and the designated point for delivering the item;and provide the delivery instructions to a mobile device associated witha delivery service.
 16. The non-transitory computer-readable medium ofclaim 15, wherein the delivery instructions include augmented realityinformation indicating the designated point for delivering the item atthe location.
 17. The non-transitory computer-readable medium of claim15, wherein the one or more instructions, when executed by the one ormore processors, further cause the one or more processors to: receiveinformation indicating that the mobile device, associated with thedelivery service, is at the location, wherein the delivery instructionsare provided to the mobile device, associated with the delivery service,based on the information indicating that the mobile device, associatedwith the delivery service, is at the location.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, that cause the one or more processors to train the machinelearning model, cause the one or more processors to: performdimensionality reduction to reduce the images of the location to afeature set of images; and train the machine learning model using thefeature set of images.
 19. The non-transitory computer-readable mediumof claim 18, wherein the one or more instructions, when executed by theone or more processors, further cause the one or more processors to:apply a classification technique to the feature set of images, theclassification technique including: a logistic regression classificationtechnique, a naïve Bayesian classifier technique, binary recursivepartitioning, or a support vector machine (SVM) classifier technique.20. The non-transitory computer-readable medium of claim 15, wherein atleast one of the images of the location are received from a mobiledevice associated with a person at the location.